MicroFuge: A Middleware Approach to Providing Performance Isolation in Cloud Storage Systems
Bibliographic record
Abstract
Most cloud providers improve resource utilization by having multiple tenants share the same resources. However, this comes at the cost of reduced isolation between tenants, which can lead to inconsistent and unpredictable performance. This performance variability is a significant impediment for tenants running services with strict latency deadlines. Providing predictable performance is particularly important for cloud storage systems. The storage system is the performance bottleneck for many cloud-based services and therefore often determines their overall performance characteristics. In this paper, we introduce MicroFuge, a new distributed caching and scheduling middleware that provides performance isolation for cloud storage systems. MicroFuge addresses the performance isolation problem by building an empirically-driven performance model of the underlying storage system based on measured data. Using this model, MicroFuge reduces deadline misses through adaptive deadline-aware cache eviction, scheduling and load-balancing policies. MicroFuge can also perform early rejection of requests that are unlikely to make their deadlines. Using workloads from the YCSB benchmark on an EC2 deployment, we show that adding MicroFuge to the storage stack substantially reduces the deadline miss rate of a distributed storage system compared to using a deadline oblivious distributed caching middleware such as Memcached.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".